Marrow FACS Notebook

Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

tissue_of_interest = "Marrow"

Load the requisite packages and some additional helper functions.

library(here)
library(useful)
library(Seurat)
library(dplyr)
library(Matrix)
save_dir = here('00_data_ingest', 'tissue_robj')

# read the metadata to get the plates we want
plate_metadata_filename = here('00_data_ingest', '00_facs_raw_data', 'metadata_FACS.csv')
plate_metadata <- read.csv(plate_metadata_filename, sep=",", header = TRUE)
colnames(plate_metadata)[1] <- "plate.barcode"
plate_metadata

Subset the metadata on the tissue.

tissue_plates = filter(plate_metadata, tissue == tissue_of_interest)[,c('plate.barcode','tissue','subtissue','mouse.sex')]
tissue_plates

Load the read count data.

#Load the gene names and set the metadata columns by opening the first file
filename = here('00_data_ingest', '00_facs_raw_data', 'FACS', paste0(tissue_of_interest, '-counts.csv'))
raw.data = read.csv(filename, sep=",", row.names=1)
# raw.data = data.frame(row.names = rownames(raw.data))
corner(raw.data)

Make a vector of plate barcodes for each cell

plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2])
head(plate.barcodes)

[[1]]
[1] "D042044"

[[2]]
[1] "D042044"

[[3]]
[1] "D042044"

[[4]]
[1] "D042044"

[[5]]
[1] "D042044"

[[6]]
[1] "D042044"

Use only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to “expand” the per-plate metadata to be per-cell.

Process the raw data and load it into the Seurat object.

# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]
# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest, 
                    min.cells = 5, min.genes = 5)
tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'
# Create metadata columns for annotations and subannotations
tiss@meta.data[,'annotation'] <- NA
tiss@meta.data[,'subannotation'] <- NA

Calculate percent ribosomal genes.

ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")

A sanity check: genes per cell vs reads per cell.

GenePlot(object = tiss, gene1 = "nReads", gene2 = "nGene", use.raw=T)

Filter out cells with few reads and few genes.

tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nReads"), 
    low.thresholds = c(500, 50000), high.thresholds = c(25000, 2000000))

Normalize the data, then regress out correlation with total reads

tiss <- NormalizeData(object = tiss)

Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|

tiss <- ScaleData(object = tiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))

[1] "Regressing out nReads"       "Regressing out percent.ribo" "Regressing out Rn45s"       

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Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec,  :
  EOF within quoted string
Quitting from lines 79-97 (Trachea_Plate.Rmd) 

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[1] "Scaling data matrix"

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tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)

Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|

Run Principal Component Analysis.

tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

PCElbowPlot(object = tiss)

Choose the number of principal components to use.

# Set number of principal components. 
n.pcs = 15

The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

# Set resolution 
res.used <- 0.5
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)

To visualize

# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=84)

# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T)

Check expression of genes of interset.

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

How big are the clusters?

table(tiss@ident)

   0    1    2    3    4    5    6    7    8    9 
1137  863  770  700  324  285  278  210  177  153 

Which markers identify a specific cluster?

clust.markers <- FindMarkers(object = tiss, ident.1 = 0, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~02m 21s      
   |+                                                 | 2 % ~02m 19s      
   |++                                                | 3 % ~02m 17s      
   |++                                                | 4 % ~02m 25s      
   |+++                                               | 5 % ~02m 23s      
   |+++                                               | 6 % ~02m 19s      
   |++++                                              | 7 % ~02m 16s      
   |++++                                              | 8 % ~02m 15s      
   |+++++                                             | 9 % ~02m 15s      
   |+++++                                             | 10% ~02m 13s      
   |++++++                                            | 11% ~02m 10s      
   |++++++                                            | 12% ~02m 08s      
   |+++++++                                           | 13% ~02m 06s      
   |+++++++                                           | 14% ~02m 05s      
   |++++++++                                          | 15% ~02m 04s      
   |++++++++                                          | 16% ~02m 02s      
   |+++++++++                                         | 17% ~02m 01s      
   |+++++++++                                         | 18% ~01m 59s      
   |++++++++++                                        | 19% ~01m 57s      
   |++++++++++                                        | 20% ~01m 56s      
   |+++++++++++                                       | 21% ~01m 54s      
   |+++++++++++                                       | 22% ~01m 54s      
   |++++++++++++                                      | 23% ~01m 52s      
   |++++++++++++                                      | 24% ~01m 51s      
   |+++++++++++++                                     | 25% ~01m 50s      
   |+++++++++++++                                     | 26% ~01m 48s      
   |++++++++++++++                                    | 27% ~01m 47s      
   |++++++++++++++                                    | 28% ~01m 46s      
   |+++++++++++++++                                   | 29% ~01m 45s      
   |+++++++++++++++                                   | 30% ~01m 44s      
   |++++++++++++++++                                  | 31% ~01m 45s      
   |++++++++++++++++                                  | 32% ~01m 45s      
   |+++++++++++++++++                                 | 33% ~01m 44s      
   |+++++++++++++++++                                | 34% ~01m 42s      
   |++++++++++++++++++                                | 35% ~01m 40s      
   |++++++++++++++++++                                | 36% ~01m 38s      
   |+++++++++++++++++++                               | 37% ~01m 37s      
   |+++++++++++++++++++                               | 38% ~01m 35s      
   |++++++++++++++++++++                              | 39% ~01m 35s      
   |++++++++++++++++++++                              | 40% ~01m 33s      
   |+++++++++++++++++++++                             | 41% ~01m 31s      
   |+++++++++++++++++++++                             | 42% ~01m 30s      
   |++++++++++++++++++++++                            | 43% ~01m 28s      
   |++++++++++++++++++++++                            | 44% ~01m 26s      
   |+++++++++++++++++++++++                           | 45% ~01m 25s      
   |+++++++++++++++++++++++                           | 46% ~01m 23s      
   |++++++++++++++++++++++++                          | 47% ~01m 21s      
   |++++++++++++++++++++++++                          | 48% ~01m 19s      
   |+++++++++++++++++++++++++                         | 49% ~01m 18s      
   |+++++++++++++++++++++++++                         | 50% ~01m 16s      
   |++++++++++++++++++++++++++                        | 51% ~01m 14s      
   |++++++++++++++++++++++++++                        | 52% ~01m 13s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 11s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 10s      
   |++++++++++++++++++++++++++++                      | 55% ~01m 08s      
   |++++++++++++++++++++++++++++                     | 56% ~01m 06s      
   |+++++++++++++++++++++++++++++                     | 57% ~01m 05s      
   |+++++++++++++++++++++++++++++                     | 58% ~01m 03s      
   |++++++++++++++++++++++++++++++                    | 59% ~01m 01s      
   |++++++++++++++++++++++++++++++                    | 60% ~60s          
   |+++++++++++++++++++++++++++++++                   | 61% ~58s          
   |+++++++++++++++++++++++++++++++                   | 62% ~57s          
   |++++++++++++++++++++++++++++++++                  | 63% ~55s          
   |++++++++++++++++++++++++++++++++                  | 64% ~54s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~52s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~50s          
   |++++++++++++++++++++++++++++++++++                | 67% ~49s          
   |++++++++++++++++++++++++++++++++++               | 68% ~47s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~46s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~44s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~43s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~41s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~40s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~38s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~37s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~35s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~34s          
   |+++++++++++++++++++++++++++++++++++++++          | 78% ~32s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~31s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~29s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~28s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~26s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~25s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~23s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~22s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~20s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~19s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~18s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~16s          
   |+++++++++++++++++++++++++++++++++++++++++++++    | 90% ~15s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~13s          
   |++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~12s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 25s

print(x = head(x= clust.markers, n = 10))

You can also compute all markers for all clusters at once. This may take some time.

tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~02m 07s      
   |++                                                | 2 % ~02m 05s      
   |++                                                | 3 % ~02m 03s      
   |+++                                               | 4 % ~02m 02s      
   |+++                                               | 5 % ~02m 01s      
   |++++                                              | 6 % ~02m 00s      
   |++++                                              | 7 % ~01m 58s      
   |+++++                                             | 8 % ~01m 57s      
   |+++++                                             | 9 % ~01m 58s      
   |++++++                                            | 11% ~01m 57s      
   |++++++                                            | 12% ~01m 55s      
   |+++++++                                           | 13% ~01m 54s      
   |+++++++                                           | 14% ~01m 52s      
   |++++++++                                          | 15% ~01m 51s      
   |++++++++                                          | 16% ~01m 49s      
   |+++++++++                                         | 17% ~01m 48s      
   |+++++++++                                         | 18% ~01m 46s      
   |++++++++++                                        | 19% ~01m 44s      
   |++++++++++                                        | 20% ~01m 43s      
   |+++++++++++                                       | 21% ~01m 41s      
   |++++++++++++                                      | 22% ~01m 40s      
   |++++++++++++                                      | 23% ~01m 39s      
   |+++++++++++++                                     | 24% ~01m 37s      
   |+++++++++++++                                     | 25% ~01m 36s      
   |++++++++++++++                                    | 26% ~01m 34s      
   |++++++++++++++                                    | 27% ~01m 33s      
   |+++++++++++++++                                   | 28% ~01m 31s      
   |+++++++++++++++                                   | 29% ~01m 30s      
   |++++++++++++++++                                  | 31% ~01m 29s      
   |++++++++++++++++                                  | 32% ~01m 28s      
   |+++++++++++++++++                                 | 33% ~01m 27s      
   |+++++++++++++++++                                 | 34% ~01m 26s      
   |++++++++++++++++++                                | 35% ~01m 25s      
   |++++++++++++++++++                                | 36% ~01m 23s      
   |+++++++++++++++++++                               | 37% ~01m 22s      
   |+++++++++++++++++++                               | 38% ~01m 21s      
   |++++++++++++++++++++                              | 39% ~01m 19s      
   |++++++++++++++++++++                              | 40% ~01m 18s      
   |+++++++++++++++++++++                             | 41% ~01m 17s      
   |++++++++++++++++++++++                            | 42% ~01m 15s      
   |++++++++++++++++++++++                            | 43% ~01m 14s      
   |+++++++++++++++++++++++                           | 44% ~01m 12s      
   |+++++++++++++++++++++++                           | 45% ~01m 11s      
   |++++++++++++++++++++++++                          | 46% ~01m 11s      
   |++++++++++++++++++++++++                          | 47% ~01m 09s      
   |+++++++++++++++++++++++++                         | 48% ~01m 08s      
   |+++++++++++++++++++++++++                         | 49% ~01m 07s      
   |++++++++++++++++++++++++++                        | 51% ~01m 05s      
   |++++++++++++++++++++++++++                        | 52% ~01m 04s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 02s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 01s      
   |++++++++++++++++++++++++++++                      | 55% ~59s          
   |++++++++++++++++++++++++++++                      | 56% ~58s          
   |+++++++++++++++++++++++++++++                     | 57% ~57s          
   |+++++++++++++++++++++++++++++                     | 58% ~55s          
   |++++++++++++++++++++++++++++++                    | 59% ~54s          
   |++++++++++++++++++++++++++++++                    | 60% ~52s          
   |+++++++++++++++++++++++++++++++                   | 61% ~51s          
   |++++++++++++++++++++++++++++++++                  | 62% ~50s          
   |++++++++++++++++++++++++++++++++                  | 63% ~48s          
   |+++++++++++++++++++++++++++++++++                 | 64% ~47s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~45s          
   |++++++++++++++++++++++++++++++++++                | 66% ~44s          
   |++++++++++++++++++++++++++++++++++                | 67% ~43s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~41s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~40s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~38s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~37s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~36s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~34s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~33s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~32s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~30s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~29s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~27s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~26s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~25s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~23s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~22s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~21s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~19s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~18s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~17s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~15s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~14s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~13s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 13s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~01m 12s      
   |++                                                | 2 % ~01m 13s      
   |++                                                | 3 % ~01m 12s      
   |+++                                               | 4 % ~01m 11s      
   |+++                                               | 5 % ~01m 10s      
   |++++                                              | 6 % ~01m 09s      
   |++++                                              | 7 % ~01m 09s      
   |+++++                                             | 8 % ~01m 08s      
   |+++++                                             | 9 % ~01m 07s      
   |++++++                                            | 10% ~01m 07s      
   |++++++                                            | 11% ~01m 07s      
   |+++++++                                           | 12% ~01m 07s      
   |+++++++                                           | 13% ~01m 06s      
   |++++++++                                          | 14% ~01m 05s      
   |++++++++                                          | 15% ~01m 04s      
   |+++++++++                                         | 16% ~01m 03s      
   |+++++++++                                         | 18% ~01m 02s      
   |++++++++++                                        | 19% ~01m 01s      
   |++++++++++                                        | 20% ~01m 00s      
   |+++++++++++                                       | 21% ~59s          
   |+++++++++++                                       | 22% ~59s          
   |++++++++++++                                      | 23% ~58s          
   |++++++++++++                                      | 24% ~57s          
   |+++++++++++++                                     | 25% ~57s          
   |+++++++++++++                                     | 26% ~56s          
   |++++++++++++++                                    | 27% ~56s          
   |++++++++++++++                                    | 28% ~55s          
   |+++++++++++++++                                   | 29% ~54s          
   |+++++++++++++++                                   | 30% ~53s          
   |++++++++++++++++                                  | 31% ~52s          
   |++++++++++++++++                                  | 32% ~52s          
   |+++++++++++++++++                                 | 33% ~51s          
   |++++++++++++++++++                                | 34% ~50s          
   |++++++++++++++++++                                | 35% ~49s          
   |+++++++++++++++++++                               | 36% ~49s          
   |+++++++++++++++++++                               | 37% ~48s          
   |++++++++++++++++++++                              | 38% ~47s          
   |++++++++++++++++++++                              | 39% ~46s          
   |+++++++++++++++++++++                             | 40% ~46s          
   |+++++++++++++++++++++                             | 41% ~45s          
   |++++++++++++++++++++++                            | 42% ~44s          
   |++++++++++++++++++++++                            | 43% ~44s          
   |+++++++++++++++++++++++                           | 44% ~44s          
   |+++++++++++++++++++++++                           | 45% ~44s          
   |++++++++++++++++++++++++                          | 46% ~43s          
   |++++++++++++++++++++++++                          | 47% ~42s          
   |+++++++++++++++++++++++++                         | 48% ~42s          
   |+++++++++++++++++++++++++                         | 49% ~41s          
   |++++++++++++++++++++++++++                        | 51% ~41s          
   |++++++++++++++++++++++++++                        | 52% ~40s          
   |+++++++++++++++++++++++++++                       | 53% ~39s          
   |+++++++++++++++++++++++++++                       | 54% ~38s          
   |++++++++++++++++++++++++++++                      | 55% ~37s          
   |++++++++++++++++++++++++++++                      | 56% ~36s          
   |+++++++++++++++++++++++++++++                     | 57% ~35s          
   |+++++++++++++++++++++++++++++                     | 58% ~34s          
   |++++++++++++++++++++++++++++++                    | 59% ~34s          
   |++++++++++++++++++++++++++++++                    | 60% ~33s          
   |+++++++++++++++++++++++++++++++                   | 61% ~32s          
   |+++++++++++++++++++++++++++++++                   | 62% ~31s          
   |++++++++++++++++++++++++++++++++                  | 63% ~30s          
   |++++++++++++++++++++++++++++++++                  | 64% ~29s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~29s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~28s          
   |++++++++++++++++++++++++++++++++++                | 67% ~27s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~26s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~25s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~24s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~23s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~23s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~22s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~21s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~20s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~20s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~19s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~18s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~17s          
   |+++++++++++++++++++++++++++++++++++++++++         | 80% ~16s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~15s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~14s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~12s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 20s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~03m 49s      
   |++                                                | 2 % ~03m 44s      
   |++                                                | 3 % ~03m 42s      
   |+++                                               | 4 % ~03m 39s      
   |+++                                               | 5 % ~03m 36s      
   |++++                                              | 6 % ~03m 33s      
   |++++                                              | 7 % ~03m 32s      
   |+++++                                             | 8 % ~03m 31s      
   |+++++                                             | 9 % ~03m 29s      
   |++++++                                            | 10% ~03m 28s      
   |++++++                                            | 11% ~03m 26s      
   |+++++++                                           | 12% ~03m 24s      
   |+++++++                                           | 13% ~03m 28s      
   |++++++++                                          | 14% ~03m 30s      
   |++++++++                                          | 15% ~03m 27s      
   |+++++++++                                         | 16% ~03m 24s      
   |+++++++++                                         | 17% ~03m 20s      
   |++++++++++                                        | 18% ~03m 22s      
   |++++++++++                                        | 19% ~03m 18s      
   |+++++++++++                                       | 20% ~03m 15s      
   |+++++++++++                                       | 21% ~03m 12s      
   |++++++++++++                                      | 22% ~03m 08s      
   |++++++++++++                                      | 23% ~03m 05s      
   |+++++++++++++                                     | 24% ~03m 04s      
   |+++++++++++++                                     | 25% ~03m 01s      
   |++++++++++++++                                    | 26% ~02m 58s      
   |++++++++++++++                                    | 27% ~02m 55s      
   |+++++++++++++++                                   | 28% ~02m 53s      
   |+++++++++++++++                                   | 29% ~02m 50s      
   |++++++++++++++++                                  | 30% ~02m 47s      
   |++++++++++++++++                                  | 31% ~02m 45s      
   |+++++++++++++++++                                 | 32% ~02m 42s      
   |+++++++++++++++++                                 | 33% ~02m 40s      
   |++++++++++++++++++                                | 34% ~02m 38s      
   |++++++++++++++++++                                | 35% ~02m 35s      
   |+++++++++++++++++++                               | 36% ~02m 33s      
   |+++++++++++++++++++                               | 37% ~02m 31s      
   |++++++++++++++++++++                              | 38% ~02m 29s      
   |++++++++++++++++++++                              | 39% ~02m 27s      
   |+++++++++++++++++++++                             | 40% ~02m 25s      
   |+++++++++++++++++++++                             | 41% ~02m 22s      
   |++++++++++++++++++++++                            | 42% ~02m 20s      
   |++++++++++++++++++++++                            | 43% ~02m 18s      
   |+++++++++++++++++++++++                           | 44% ~02m 15s      
   |+++++++++++++++++++++++                           | 45% ~02m 13s      
   |++++++++++++++++++++++++                          | 46% ~02m 11s      
   |++++++++++++++++++++++++                          | 47% ~02m 09s      
   |+++++++++++++++++++++++++                         | 48% ~02m 07s      
   |+++++++++++++++++++++++++                         | 49% ~02m 05s      
   |++++++++++++++++++++++++++                        | 51% ~02m 03s      
   |++++++++++++++++++++++++++                        | 52% ~01m 60s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 58s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 56s      
   |++++++++++++++++++++++++++++                      | 55% ~01m 53s      
   |++++++++++++++++++++++++++++                      | 56% ~01m 51s      
   |+++++++++++++++++++++++++++++                     | 57% ~01m 48s      
   |+++++++++++++++++++++++++++++                     | 58% ~01m 46s      
   |++++++++++++++++++++++++++++++                    | 59% ~01m 43s      
   |++++++++++++++++++++++++++++++                    | 60% ~01m 41s      
   |+++++++++++++++++++++++++++++++                   | 61% ~01m 39s      
   |+++++++++++++++++++++++++++++++                   | 62% ~01m 36s      
   |++++++++++++++++++++++++++++++++                  | 63% ~01m 34s      
   |++++++++++++++++++++++++++++++++                  | 64% ~01m 31s      
   |+++++++++++++++++++++++++++++++++                 | 65% ~01m 29s      
   |+++++++++++++++++++++++++++++++++                 | 66% ~01m 26s      
   |++++++++++++++++++++++++++++++++++                | 67% ~01m 24s      
   |++++++++++++++++++++++++++++++++++                | 68% ~01m 21s      
   |+++++++++++++++++++++++++++++++++++               | 69% ~01m 19s      
   |+++++++++++++++++++++++++++++++++++               | 70% ~01m 16s      
   |++++++++++++++++++++++++++++++++++++              | 71% ~01m 14s      
   |++++++++++++++++++++++++++++++++++++              | 72% ~01m 11s      
   |+++++++++++++++++++++++++++++++++++++             | 73% ~01m 09s      

Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec,  :
  EOF within quoted string
Quitting from lines 56-58 (Bladder_Plate.Rmd) 

   |+++++++++++++++++++++++++++++++++++++             | 74% ~01m 06s      
   |++++++++++++++++++++++++++++++++++++++            | 75% ~01m 04s      
   |++++++++++++++++++++++++++++++++++++++            | 76% ~01m 01s      
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~59s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~57s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~54s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~52s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~49s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~47s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~44s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~42s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~39s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~37s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~34s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~32s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~29s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~26s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~24s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~21s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~19s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~16s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~13s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 04m 31s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~02m 19s      
   |++                                                | 2 % ~02m 08s      
   |++                                                | 3 % ~02m 04s      
   |+++                                               | 4 % ~01m 60s      
   |+++                                               | 5 % ~01m 60s      
   |++++                                              | 6 % ~01m 57s      
   |++++                                              | 8 % ~01m 54s      
   |+++++                                             | 9 % ~01m 51s      
   |+++++                                             | 10% ~01m 49s      
   |++++++                                            | 11% ~01m 47s      
   |++++++                                            | 12% ~01m 45s      
   |+++++++                                           | 13% ~01m 44s      
   |+++++++                                           | 14% ~01m 42s      
   |++++++++                                          | 15% ~01m 41s      
   |+++++++++                                         | 16% ~01m 40s      
   |+++++++++                                         | 17% ~01m 39s      
   |++++++++++                                        | 18% ~01m 37s      
   |++++++++++                                        | 19% ~01m 36s      
   |+++++++++++                                       | 20% ~01m 34s      
   |+++++++++++                                       | 22% ~01m 33s      
   |++++++++++++                                      | 23% ~01m 32s      
   |++++++++++++                                      | 24% ~01m 30s      
   |+++++++++++++                                     | 25% ~01m 29s      
   |+++++++++++++                                     | 26% ~01m 28s      
   |++++++++++++++                                    | 27% ~01m 27s      
   |++++++++++++++                                    | 28% ~01m 25s      
   |+++++++++++++++                                   | 29% ~01m 24s      
   |++++++++++++++++                                  | 30% ~01m 24s      
   |++++++++++++++++                                  | 31% ~01m 24s      
   |+++++++++++++++++                                 | 32% ~01m 22s      
   |+++++++++++++++++                                 | 33% ~01m 21s      
   |++++++++++++++++++                                | 34% ~01m 19s      
   |++++++++++++++++++                                | 35% ~01m 18s      
   |+++++++++++++++++++                               | 37% ~01m 16s      
   |+++++++++++++++++++                               | 38% ~01m 15s      
   |++++++++++++++++++++                              | 39% ~01m 13s      
   |++++++++++++++++++++                              | 40% ~01m 12s      
   |+++++++++++++++++++++                             | 41% ~01m 11s      
   |+++++++++++++++++++++                             | 42% ~01m 09s      
   |++++++++++++++++++++++                            | 43% ~01m 08s      
   |+++++++++++++++++++++++                           | 44% ~01m 07s      
   |+++++++++++++++++++++++                           | 45% ~01m 05s      
   |++++++++++++++++++++++++                          | 46% ~01m 04s      
   |++++++++++++++++++++++++                          | 47% ~01m 03s      
   |+++++++++++++++++++++++++                         | 48% ~01m 01s      
   |+++++++++++++++++++++++++                         | 49% ~60s          
   |++++++++++++++++++++++++++                        | 51% ~59s          
   |++++++++++++++++++++++++++                        | 52% ~57s          
   |+++++++++++++++++++++++++++                       | 53% ~56s          
   |+++++++++++++++++++++++++++                       | 54% ~54s          
   |++++++++++++++++++++++++++++                      | 55% ~53s          
   |++++++++++++++++++++++++++++                      | 56% ~52s          
   |+++++++++++++++++++++++++++++                     | 57% ~50s          
   |++++++++++++++++++++++++++++++                    | 58% ~49s          
   |++++++++++++++++++++++++++++++                    | 59% ~47s          
   |+++++++++++++++++++++++++++++++                   | 60% ~46s          
   |+++++++++++++++++++++++++++++++                   | 61% ~45s          
   |++++++++++++++++++++++++++++++++                  | 62% ~43s          
   |++++++++++++++++++++++++++++++++                  | 63% ~42s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~41s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~39s          
   |++++++++++++++++++++++++++++++++++                | 67% ~38s          
   |++++++++++++++++++++++++++++++++++                | 68% ~36s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~35s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~34s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~33s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~31s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~30s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~29s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~28s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~26s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~25s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~24s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~23s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~21s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~20s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~19s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~18s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~17s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~15s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~14s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~12s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 51s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~01m 48s      
   |++                                                | 2 % ~01m 46s      
   |++                                                | 3 % ~01m 43s      
   |+++                                               | 4 % ~01m 44s      
   |+++                                               | 5 % ~01m 44s      
   |++++                                              | 6 % ~01m 43s      
   |++++                                              | 7 % ~01m 42s      
   |+++++                                             | 8 % ~01m 44s      
   |+++++                                             | 9 % ~01m 43s      
   |++++++                                            | 11% ~01m 42s      
   |++++++                                            | 12% ~01m 41s      
   |+++++++                                           | 13% ~01m 42s      
   |+++++++                                           | 14% ~01m 42s      
   |++++++++                                          | 15% ~01m 41s      
   |++++++++                                          | 16% ~01m 41s      
   |+++++++++                                         | 17% ~01m 41s      
   |+++++++++                                         | 18% ~01m 40s      
   |++++++++++                                        | 19% ~01m 38s      
   |++++++++++                                        | 20% ~01m 37s      
   |+++++++++++                                       | 21% ~01m 35s      
   |++++++++++++                                      | 22% ~01m 33s      
   |++++++++++++                                      | 23% ~01m 32s      
   |+++++++++++++                                     | 24% ~01m 30s      
   |+++++++++++++                                     | 25% ~01m 29s      
   |++++++++++++++                                    | 26% ~01m 28s      
   |++++++++++++++                                    | 27% ~01m 26s      
   |+++++++++++++++                                   | 28% ~01m 25s      
   |+++++++++++++++                                   | 29% ~01m 23s      
   |++++++++++++++++                                  | 31% ~01m 21s      
   |++++++++++++++++                                  | 32% ~01m 20s      
   |+++++++++++++++++                                 | 33% ~01m 18s      
   |+++++++++++++++++                                 | 34% ~01m 17s      
   |++++++++++++++++++                                | 35% ~01m 16s      
   |++++++++++++++++++                                | 36% ~01m 14s      
   |+++++++++++++++++++                               | 37% ~01m 13s      
   |+++++++++++++++++++                               | 38% ~01m 11s      
   |++++++++++++++++++++                              | 39% ~01m 10s      
   |++++++++++++++++++++                              | 40% ~01m 09s      
   |+++++++++++++++++++++                             | 41% ~01m 07s      
   |++++++++++++++++++++++                            | 42% ~01m 06s      
   |++++++++++++++++++++++                            | 43% ~01m 05s      
   |+++++++++++++++++++++++                           | 44% ~01m 03s      
   |+++++++++++++++++++++++                           | 45% ~01m 02s      
   |++++++++++++++++++++++++                          | 46% ~01m 00s      
   |++++++++++++++++++++++++                          | 47% ~59s          
   |+++++++++++++++++++++++++                         | 48% ~57s          
   |+++++++++++++++++++++++++                         | 49% ~56s          
   |++++++++++++++++++++++++++                        | 51% ~54s          
   |++++++++++++++++++++++++++                        | 52% ~53s          
   |+++++++++++++++++++++++++++                       | 53% ~52s          
   |+++++++++++++++++++++++++++                       | 54% ~50s          
   |++++++++++++++++++++++++++++                      | 55% ~49s          
   |++++++++++++++++++++++++++++                      | 56% ~48s          
   |+++++++++++++++++++++++++++++                     | 57% ~46s          
   |+++++++++++++++++++++++++++++                     | 58% ~45s          
   |++++++++++++++++++++++++++++++                    | 59% ~44s          
   |++++++++++++++++++++++++++++++                    | 60% ~43s          
   |+++++++++++++++++++++++++++++++                   | 61% ~41s          
   |++++++++++++++++++++++++++++++++                  | 62% ~40s          
   |++++++++++++++++++++++++++++++++                  | 63% ~39s          
   |+++++++++++++++++++++++++++++++++                 | 64% ~38s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~36s          
   |++++++++++++++++++++++++++++++++++                | 66% ~35s          
   |++++++++++++++++++++++++++++++++++                | 67% ~34s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~33s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~32s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~31s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~29s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~28s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~27s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~26s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~25s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~24s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~23s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~21s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~20s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~19s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~18s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~17s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~16s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~15s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~14s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~12s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~10s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 40s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~02m 13s      
   |++                                                | 2 % ~02m 15s      
   |++                                                | 3 % ~02m 30s      
   |+++                                               | 4 % ~02m 33s      
   |+++                                               | 5 % ~02m 34s      
   |++++                                              | 6 % ~02m 37s      
   |++++                                              | 7 % ~02m 34s      
   |+++++                                             | 8 % ~02m 33s      
   |+++++                                             | 9 % ~02m 30s      
   |++++++                                            | 11% ~02m 27s      
   |++++++                                            | 12% ~02m 24s      
   |+++++++                                           | 13% ~02m 20s      
   |+++++++                                           | 14% ~02m 18s      
   |++++++++                                          | 15% ~02m 17s      
   |++++++++                                          | 16% ~02m 15s      
   |+++++++++                                         | 17% ~02m 14s      
   |+++++++++                                         | 18% ~02m 14s      
   |++++++++++                                        | 19% ~02m 14s      
   |++++++++++                                        | 20% ~02m 15s      
   |+++++++++++                                       | 21% ~02m 15s      
   |++++++++++++                                      | 22% ~02m 14s      
   |++++++++++++                                      | 23% ~02m 13s      
   |+++++++++++++                                     | 24% ~02m 12s      
   |+++++++++++++                                     | 25% ~02m 10s      
   |++++++++++++++                                    | 26% ~02m 09s      
   |++++++++++++++                                    | 27% ~02m 09s      
   |+++++++++++++++                                   | 28% ~02m 07s      
   |+++++++++++++++                                   | 29% ~02m 06s      
   |++++++++++++++++                                  | 31% ~02m 05s      
   |++++++++++++++++                                  | 32% ~02m 04s      
   |+++++++++++++++++                                 | 33% ~02m 02s      
   |+++++++++++++++++                                 | 34% ~02m 01s      
   |++++++++++++++++++                                | 35% ~01m 60s      
   |++++++++++++++++++                                | 36% ~01m 59s      
   |+++++++++++++++++++                               | 37% ~01m 57s      
   |+++++++++++++++++++                               | 38% ~01m 56s      
   |++++++++++++++++++++                              | 39% ~01m 54s      
   |++++++++++++++++++++                              | 40% ~01m 53s      
   |+++++++++++++++++++++                             | 41% ~01m 51s      
   |++++++++++++++++++++++                            | 42% ~01m 50s      
   |++++++++++++++++++++++                            | 43% ~01m 49s      
   |+++++++++++++++++++++++                           | 44% ~01m 47s      
   |+++++++++++++++++++++++                           | 45% ~01m 45s      
   |++++++++++++++++++++++++                          | 46% ~01m 43s      
   |++++++++++++++++++++++++                          | 47% ~01m 42s      
   |+++++++++++++++++++++++++                         | 48% ~01m 41s      
   |+++++++++++++++++++++++++                         | 49% ~01m 39s      
   |++++++++++++++++++++++++++                        | 51% ~01m 38s      
   |++++++++++++++++++++++++++                        | 52% ~01m 36s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 34s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 32s      
   |++++++++++++++++++++++++++++                      | 55% ~01m 32s      
   |++++++++++++++++++++++++++++                      | 56% ~01m 30s      
   |+++++++++++++++++++++++++++++                     | 57% ~01m 28s      
   |+++++++++++++++++++++++++++++                     | 58% ~01m 26s      
   |++++++++++++++++++++++++++++++                    | 59% ~01m 24s      
   |++++++++++++++++++++++++++++++                    | 60% ~01m 22s      
   |+++++++++++++++++++++++++++++++                   | 61% ~01m 20s      
   |++++++++++++++++++++++++++++++++                  | 62% ~01m 18s      
   |++++++++++++++++++++++++++++++++                  | 63% ~01m 16s      
   |+++++++++++++++++++++++++++++++++                 | 64% ~01m 14s      
   |+++++++++++++++++++++++++++++++++                 | 65% ~01m 13s      
   |++++++++++++++++++++++++++++++++++                | 66% ~01m 11s      
   |++++++++++++++++++++++++++++++++++                | 67% ~01m 09s      
   |+++++++++++++++++++++++++++++++++++               | 68% ~01m 07s      
   |+++++++++++++++++++++++++++++++++++               | 69% ~01m 06s      
   |++++++++++++++++++++++++++++++++++++              | 71% ~01m 04s      
   |++++++++++++++++++++++++++++++++++++              | 72% ~01m 02s      
   |+++++++++++++++++++++++++++++++++++++             | 73% ~60s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~58s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~56s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~53s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~51s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~49s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~46s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~44s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~42s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~39s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~37s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~35s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~32s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~30s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~28s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~25s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~23s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~21s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~18s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~16s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~14s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~12s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 39s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~03m 37s      
   |++                                                | 2 % ~03m 13s      
   |++                                                | 3 % ~03m 34s      
   |+++                                               | 4 % ~03m 12s      
   |+++                                               | 5 % ~03m 01s      
   |++++                                              | 6 % ~02m 51s      
   |++++                                              | 7 % ~02m 43s      
   |+++++                                             | 8 % ~02m 37s      
   |+++++                                             | 9 % ~02m 32s      
   |++++++                                            | 10% ~02m 29s      
   |++++++                                            | 11% ~02m 26s      
   |+++++++                                           | 12% ~02m 29s      
   |+++++++                                           | 13% ~02m 29s      
   |++++++++                                          | 14% ~02m 25s      
   |++++++++                                          | 15% ~02m 22s      
   |+++++++++                                         | 16% ~02m 19s      
   |+++++++++                                         | 18% ~02m 16s      
   |++++++++++                                        | 19% ~02m 13s      
   |++++++++++                                        | 20% ~02m 11s      
   |+++++++++++                                       | 21% ~02m 09s      
   |+++++++++++                                       | 22% ~02m 06s      
   |++++++++++++                                      | 23% ~02m 04s      
   |++++++++++++                                      | 24% ~02m 02s      
   |+++++++++++++                                     | 25% ~01m 59s      
   |+++++++++++++                                     | 26% ~01m 57s      
   |++++++++++++++                                    | 27% ~01m 55s      
   |++++++++++++++                                    | 28% ~01m 53s      
   |+++++++++++++++                                   | 29% ~01m 51s      
   |+++++++++++++++                                   | 30% ~01m 49s      
   |++++++++++++++++                                  | 31% ~01m 47s      
   |++++++++++++++++                                  | 32% ~01m 46s      
   |+++++++++++++++++                                 | 33% ~01m 44s      
   |++++++++++++++++++                                | 34% ~01m 42s      
   |++++++++++++++++++                                | 35% ~01m 40s      
   |+++++++++++++++++++                               | 36% ~01m 38s      
   |+++++++++++++++++++                               | 37% ~01m 37s      
   |++++++++++++++++++++                              | 38% ~01m 35s      
   |++++++++++++++++++++                              | 39% ~01m 34s      
   |+++++++++++++++++++++                             | 40% ~01m 32s      
   |+++++++++++++++++++++                             | 41% ~01m 30s      
   |++++++++++++++++++++++                            | 42% ~01m 29s      
   |++++++++++++++++++++++                            | 43% ~01m 27s      
   |+++++++++++++++++++++++                           | 44% ~01m 26s      
   |+++++++++++++++++++++++                           | 45% ~01m 26s      
   |++++++++++++++++++++++++                          | 46% ~01m 24s      
   |++++++++++++++++++++++++                          | 47% ~01m 23s      
   |+++++++++++++++++++++++++                         | 48% ~01m 21s      
   |+++++++++++++++++++++++++                         | 49% ~01m 19s      
   |++++++++++++++++++++++++++                        | 51% ~01m 17s      
   |++++++++++++++++++++++++++                        | 52% ~01m 15s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 14s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 13s      
   |++++++++++++++++++++++++++++                      | 55% ~01m 12s      
   |++++++++++++++++++++++++++++                      | 56% ~01m 10s      
   |+++++++++++++++++++++++++++++                     | 57% ~01m 08s      
   |+++++++++++++++++++++++++++++                     | 58% ~01m 06s      
   |++++++++++++++++++++++++++++++                    | 59% ~01m 05s      
   |++++++++++++++++++++++++++++++                    | 60% ~01m 03s      
   |+++++++++++++++++++++++++++++++                   | 61% ~01m 01s      
   |+++++++++++++++++++++++++++++++                   | 62% ~60s          
   |++++++++++++++++++++++++++++++++                  | 63% ~58s          
   |++++++++++++++++++++++++++++++++                  | 64% ~56s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~55s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~53s          
   |++++++++++++++++++++++++++++++++++                | 67% ~52s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~50s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~48s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~47s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~45s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~44s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~42s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~40s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~39s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~37s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~35s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~34s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~32s          
   |+++++++++++++++++++++++++++++++++++++++++         | 80% ~30s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~29s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~27s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~26s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~24s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~22s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~21s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~19s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~17s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~16s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~14s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 31s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~03m 10s      
   |++                                                | 2 % ~03m 08s      
   |++                                                | 3 % ~03m 07s      
   |+++                                               | 4 % ~03m 03s      
   |+++                                               | 5 % ~03m 02s      
   |++++                                              | 6 % ~03m 02s      
   |++++                                              | 7 % ~03m 00s      
   |+++++                                             | 8 % ~02m 58s      
   |+++++                                             | 9 % ~02m 56s      
   |++++++                                            | 10% ~02m 54s      
   |++++++                                            | 11% ~02m 53s      
   |+++++++                                           | 12% ~02m 52s      
   |+++++++                                           | 13% ~02m 50s      
   |++++++++                                          | 14% ~02m 49s      
   |++++++++                                          | 15% ~02m 46s      
   |+++++++++                                         | 16% ~02m 45s      
   |+++++++++                                         | 17% ~02m 44s      
   |++++++++++                                        | 18% ~02m 42s      
   |++++++++++                                        | 19% ~02m 40s      
   |+++++++++++                                       | 20% ~02m 37s      
   |+++++++++++                                       | 21% ~02m 35s      
   |++++++++++++                                      | 22% ~02m 33s      
   |++++++++++++                                      | 23% ~02m 32s      
   |+++++++++++++                                     | 24% ~02m 30s      
   |+++++++++++++                                     | 25% ~02m 30s      
   |++++++++++++++                                    | 26% ~02m 28s      
   |++++++++++++++                                    | 27% ~02m 27s      
   |+++++++++++++++                                   | 28% ~02m 25s      
   |+++++++++++++++                                   | 29% ~02m 23s      
   |++++++++++++++++                                  | 30% ~02m 21s      
   |++++++++++++++++                                  | 31% ~02m 19s      
   |+++++++++++++++++                                 | 32% ~02m 17s      
   |+++++++++++++++++                                 | 33% ~02m 15s      
   |++++++++++++++++++                                | 34% ~02m 13s      
   |++++++++++++++++++                                | 35% ~02m 11s      
   |+++++++++++++++++++                               | 36% ~02m 09s      
   |+++++++++++++++++++                               | 37% ~02m 07s      
   |++++++++++++++++++++                              | 38% ~02m 05s      
   |++++++++++++++++++++                              | 39% ~02m 03s      
   |+++++++++++++++++++++                             | 40% ~02m 01s      
   |+++++++++++++++++++++                             | 41% ~01m 60s      
   |++++++++++++++++++++++                            | 42% ~01m 57s      
   |++++++++++++++++++++++                            | 43% ~01m 55s      
   |+++++++++++++++++++++++                           | 44% ~01m 53s      
   |+++++++++++++++++++++++                           | 45% ~01m 51s      
   |++++++++++++++++++++++++                          | 46% ~01m 49s      
   |++++++++++++++++++++++++                          | 47% ~01m 47s      
   |+++++++++++++++++++++++++                         | 48% ~01m 46s      
   |+++++++++++++++++++++++++                         | 49% ~01m 44s      
   |++++++++++++++++++++++++++                        | 51% ~01m 41s      
   |++++++++++++++++++++++++++                        | 52% ~01m 39s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 37s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 35s      
   |++++++++++++++++++++++++++++                      | 55% ~01m 33s      
   |++++++++++++++++++++++++++++                      | 56% ~01m 30s      
   |+++++++++++++++++++++++++++++                     | 57% ~01m 28s      
   |+++++++++++++++++++++++++++++                     | 58% ~01m 26s      
   |++++++++++++++++++++++++++++++                    | 59% ~01m 24s      
   |++++++++++++++++++++++++++++++                    | 60% ~01m 21s      
   |+++++++++++++++++++++++++++++++                   | 61% ~01m 19s      
   |+++++++++++++++++++++++++++++++                   | 62% ~01m 17s      
   |++++++++++++++++++++++++++++++++                  | 63% ~01m 15s      
   |++++++++++++++++++++++++++++++++                  | 64% ~01m 12s      
   |+++++++++++++++++++++++++++++++++                 | 65% ~01m 10s      
   |+++++++++++++++++++++++++++++++++                 | 66% ~01m 08s      
   |++++++++++++++++++++++++++++++++++                | 67% ~01m 06s      
   |++++++++++++++++++++++++++++++++++                | 68% ~01m 04s      
   |+++++++++++++++++++++++++++++++++++               | 69% ~01m 02s      
   |+++++++++++++++++++++++++++++++++++               | 70% ~60s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~58s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~56s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~53s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~51s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~49s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~47s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~45s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~43s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~41s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~39s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~37s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~35s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~33s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~31s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~29s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~27s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~25s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~23s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~21s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~19s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~17s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~15s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 04s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~01m 51s      
   |++                                                | 2 % ~01m 47s      
   |++                                                | 3 % ~01m 46s      
   |+++                                               | 4 % ~01m 45s      
   |+++                                               | 5 % ~01m 45s      
   |++++                                              | 6 % ~01m 43s      
   |++++                                              | 7 % ~01m 42s      
   |+++++                                             | 8 % ~01m 41s      
   |+++++                                             | 9 % ~01m 40s      
   |++++++                                            | 10% ~01m 39s      
   |++++++                                            | 11% ~01m 38s      
   |+++++++                                           | 12% ~01m 37s      
   |+++++++                                           | 13% ~01m 36s      
   |++++++++                                          | 14% ~01m 34s      
   |++++++++                                          | 15% ~01m 33s      
   |+++++++++                                         | 16% ~01m 32s      
   |+++++++++                                         | 17% ~01m 31s      
   |++++++++++                                        | 18% ~01m 30s      
   |++++++++++                                        | 19% ~01m 29s      
   |+++++++++++                                       | 20% ~01m 28s      
   |+++++++++++                                       | 21% ~01m 27s      
   |++++++++++++                                      | 22% ~01m 26s      
   |++++++++++++                                      | 23% ~01m 25s      
   |+++++++++++++                                     | 24% ~01m 24s      
   |+++++++++++++                                     | 25% ~01m 23s      
   |++++++++++++++                                    | 26% ~01m 22s      
   |++++++++++++++                                    | 27% ~01m 20s      
   |+++++++++++++++                                   | 28% ~01m 19s      
   |+++++++++++++++                                   | 29% ~01m 18s      
   |++++++++++++++++                                  | 30% ~01m 17s      
   |++++++++++++++++                                  | 31% ~01m 16s      
   |+++++++++++++++++                                 | 32% ~01m 15s      
   |+++++++++++++++++                                 | 33% ~01m 14s      
   |++++++++++++++++++                                | 34% ~01m 13s      
   |++++++++++++++++++                                | 35% ~01m 12s      
   |+++++++++++++++++++                               | 36% ~01m 11s      
   |+++++++++++++++++++                               | 37% ~01m 10s      
   |++++++++++++++++++++                              | 38% ~01m 09s      
   |++++++++++++++++++++                              | 39% ~01m 09s      
   |+++++++++++++++++++++                             | 40% ~01m 07s      
   |+++++++++++++++++++++                             | 41% ~01m 07s      
   |++++++++++++++++++++++                            | 42% ~01m 06s      
   |++++++++++++++++++++++                            | 43% ~01m 05s      
   |+++++++++++++++++++++++                           | 44% ~01m 04s      
   |+++++++++++++++++++++++                           | 45% ~01m 03s      
   |++++++++++++++++++++++++                          | 46% ~01m 01s      
   |++++++++++++++++++++++++                          | 47% ~01m 00s      
   |+++++++++++++++++++++++++                         | 48% ~59s          
   |+++++++++++++++++++++++++                         | 49% ~58s          
   |++++++++++++++++++++++++++                        | 51% ~57s          
   |++++++++++++++++++++++++++                        | 52% ~56s          
   |+++++++++++++++++++++++++++                       | 53% ~56s          
   |+++++++++++++++++++++++++++                       | 54% ~54s          
   |++++++++++++++++++++++++++++                      | 55% ~53s          
   |++++++++++++++++++++++++++++                      | 56% ~52s          
   |+++++++++++++++++++++++++++++                     | 57% ~51s          
   |+++++++++++++++++++++++++++++                     | 58% ~50s          
   |++++++++++++++++++++++++++++++                    | 59% ~48s          
   |++++++++++++++++++++++++++++++                    | 60% ~47s          
   |+++++++++++++++++++++++++++++++                   | 61% ~46s          
   |+++++++++++++++++++++++++++++++                   | 62% ~45s          
   |++++++++++++++++++++++++++++++++                  | 63% ~43s          
   |++++++++++++++++++++++++++++++++                  | 64% ~42s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~41s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~40s          
   |++++++++++++++++++++++++++++++++++                | 67% ~39s          
   |++++++++++++++++++++++++++++++++++                | 68% ~38s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~37s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~36s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~35s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~35s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~34s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~33s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~32s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~30s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~29s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~28s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~27s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~25s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~24s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~23s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~21s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~20s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~19s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~18s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~16s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~15s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~14s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~13s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 05s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~01m 51s      
   |++                                                | 2 % ~01m 54s      
   |++                                                | 3 % ~01m 55s      
   |+++                                               | 4 % ~01m 53s      
   |+++                                               | 5 % ~01m 53s      
   |++++                                              | 6 % ~01m 54s      
   |++++                                              | 7 % ~01m 56s      
   |+++++                                             | 8 % ~01m 56s      
   |+++++                                             | 9 % ~01m 57s      
   |++++++                                            | 10% ~01m 56s      
   |++++++                                            | 11% ~01m 54s      
   |+++++++                                           | 12% ~01m 52s      
   |+++++++                                           | 13% ~01m 51s      
   |++++++++                                          | 14% ~01m 51s      
   |++++++++                                          | 15% ~01m 51s      
   |+++++++++                                         | 16% ~01m 50s      
   |+++++++++                                         | 18% ~01m 48s      
   |++++++++++                                        | 19% ~01m 46s      
   |++++++++++                                        | 20% ~01m 44s      
   |+++++++++++                                       | 21% ~01m 42s      
   |+++++++++++                                       | 22% ~01m 40s      
   |++++++++++++                                      | 23% ~01m 39s      
   |++++++++++++                                      | 24% ~01m 37s      
   |+++++++++++++                                     | 25% ~01m 35s      
   |+++++++++++++                                     | 26% ~01m 34s      
   |++++++++++++++                                    | 27% ~01m 33s      
   |++++++++++++++                                    | 28% ~01m 31s      
   |+++++++++++++++                                   | 29% ~01m 30s      
   |+++++++++++++++                                   | 30% ~01m 29s      
   |++++++++++++++++                                  | 31% ~01m 27s      
   |++++++++++++++++                                  | 32% ~01m 26s      
   |+++++++++++++++++                                 | 33% ~01m 24s      
   |++++++++++++++++++                                | 34% ~01m 23s      
   |++++++++++++++++++                                | 35% ~01m 22s      
   |+++++++++++++++++++                               | 36% ~01m 20s      
   |+++++++++++++++++++                               | 37% ~01m 18s      
   |++++++++++++++++++++                              | 38% ~01m 17s      
   |++++++++++++++++++++                              | 39% ~01m 16s      
   |+++++++++++++++++++++                             | 40% ~01m 14s      
   |+++++++++++++++++++++                             | 41% ~01m 13s      
   |++++++++++++++++++++++                            | 42% ~01m 12s      
   |++++++++++++++++++++++                            | 43% ~01m 10s      
   |+++++++++++++++++++++++                           | 44% ~01m 09s      
   |+++++++++++++++++++++++                           | 45% ~01m 07s      
   |++++++++++++++++++++++++                          | 46% ~01m 06s      
   |++++++++++++++++++++++++                          | 47% ~01m 05s      
   |+++++++++++++++++++++++++                         | 48% ~01m 03s      
   |+++++++++++++++++++++++++                         | 49% ~01m 02s      
   |++++++++++++++++++++++++++                        | 51% ~01m 01s      
   |++++++++++++++++++++++++++                        | 52% ~59s          
   |+++++++++++++++++++++++++++                       | 53% ~58s          
   |+++++++++++++++++++++++++++                       | 54% ~57s          
   |++++++++++++++++++++++++++++                      | 55% ~56s          
   |++++++++++++++++++++++++++++                      | 56% ~55s          
   |+++++++++++++++++++++++++++++                     | 57% ~53s          
   |+++++++++++++++++++++++++++++                     | 58% ~52s          
   |++++++++++++++++++++++++++++++                    | 59% ~51s          
   |++++++++++++++++++++++++++++++                    | 60% ~49s          
   |+++++++++++++++++++++++++++++++                   | 61% ~48s          
   |+++++++++++++++++++++++++++++++                   | 62% ~47s          
   |++++++++++++++++++++++++++++++++                  | 63% ~45s          
   |++++++++++++++++++++++++++++++++                  | 64% ~44s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~43s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~41s          
   |++++++++++++++++++++++++++++++++++                | 67% ~40s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~39s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~38s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~36s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~35s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~34s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~33s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~31s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~30s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~29s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~28s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~26s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~25s          
   |+++++++++++++++++++++++++++++++++++++++++         | 80% ~24s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~23s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~22s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~20s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~19s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~18s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~17s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~15s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~14s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~13s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 04s

head(tiss.markers)

Display the top markers you computed above.

tiss.markers %>% group_by(cluster) %>% top_n(5, avg_diff)

Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:

0: alpha 1: beta 2: beta 3: exocrine 4: duct 5: delta 6: gamma 7: endothelial 8: immune 9: stellate

# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
annotation <- c("hematopoietic stem cell", "B cell", "B cell", "neutrophil", "granulocyte", "monocyte", "B cell", "T_NK", "neutrophil", "Fraction A pre-pro B cell", "hematopoietic stem cell")
cell_ontology_id <- c("CL:0000037", "CL:0000236", "CL:0000236", "CL:0000775", "CL:0000094", "CL:0000576", "CL:0000236", NA, "CL:0000775", "CL:0002045", "CL:0000037")
  
tiss@meta.data[,'annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = annotation)

The following `from` values were not present in `x`: 10

tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)

The following `from` values were not present in `x`: 10

tiss@meta.data[tiss@cell.names,'annotation'] <- as.character(tiss@meta.data$annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='annotation')

Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.

TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")

Print a table showing the count of cells in each identity category from each plate.

table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))

   
    D041912 D042044 D042193 D042467 D042479 D045139 D045140 MAA000409 MAA000596 MAA000600 MAA000639 MAA000652 MAA000844 MAA000848
  0       0       1       1       3       1       0       1         1       215       194         0         0         0       321
  1       0     103      87      30      97       0       0         0         0         0         0       166       101         0
  2     143       2       5       1       1      66     142       211         0         3       179         4         7         0
  3       2      88     106      44      52       0       1         0         1         2         0        47        67         0
  4      48       1       8       3       3      43      60        51         0         0        45         0         7         0
  5       0      79       9       3      76       0       0         0         1         0         0        42        10         0
  6       6       0      74      38       3       3       1         1         0         0         0         0        65         0
  7      69       0       2       0       0      15      10        58         0         0        46         1         2         0
  8       2      31       2       2      15       0       0         0         0         0         0        39        10         0
  9       0       0       1       0       0       0       0         0        65        47         0         0         0        28
   
    MAA001842 MAA001844 MAA001883 MAA001884 MAA001887 MAA001888
  0         2       187         0         0       208         2
  1         9         1       147       114         1         7
  2         0         2         0         0         3         1
  3        35         6        37       178         5        29
  4         2        28         0         1        16         8
  5         2         1        36        17         7         2
  6         4         3         2         1         0        77
  7         0         3         0         0         4         0
  8         1         0        26        37         0        12
  9         0         6         0         0         5         1

Subset and iterate

We can repeat the above analysis on a subset of cells, defined using cluster IDs or some other metadata. This is a good way to drill down and find substructure.

Subset == T and NK cells (Cluster 7)

# Subset data based on cluster id
subtiss7 <- SubsetData(object = tiss, ident.use = c(7), do.center = F, do.scale = F, cells.use = )
# To subset data based on annotation or other metadata, you can explicitly pass cell names
# anno = 'exocrine cells'
# cells.to.use = tiss@cell.names[which(tiss@meta.data$annotation == anno)]
# subtiss7 <- SubsetData(object = tiss, cells.use = cells.to.use, do.center = F, do.scale = F)

subtiss7 <- NormalizeData(object = subtiss7)

Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|

subtiss7 <- ScaleData(object = subtiss7, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))

[1] "Regressing out nReads"       "Regressing out percent.ribo" "Regressing out Rn45s"       

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[1] "Scaling data matrix"

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subtiss7 <- FindVariableGenes(object = subtiss7, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.8)

Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|

subtiss7 <- RunPCA(object = subtiss7, pcs.compute = 20)

[1] "PC1"
 [1] "Mcm6"    "Mcm3"    "Prtn3"   "Mcm4"    "Mcm7"    "Rrm1"    "Elane"   "Mcm2"    "Gstm1"   "Gatm"    "Rrm2"    "Pdia3"  
[13] "Uhrf1"   "Emilin1" "Man2b1"  "Sdha"    "Hat1"    "Eif3b"   "Spcs3"   "Srgn"    "Ampd2"   "Paics"   "Ctsg"    "Nkg7"   
[25] "Lrrc33"  "Rpa1"    "Ddx1"    "Lig1"    "Sdf2l1"  "Hspa9"  
[1] ""
 [1] "Cenpa"         "Malat1"        "Cenpf"         "Ube2c"         "Ifitm6"        "Cdc20"         "Slpi"         
 [8] "Plbd1"         "Ccnb1"         "Lgals3"        "Inhba"         "Ltf"           "Ifitm3"        "Aspm"         
[15] "Tubb4b"        "Cd74"          "Hn1"           "Ear2"          "Ly6a"          "Fam64a"        "St3gal5"      
[22] "Tuba1c"        "Ly6d"          "Txndc5"        "Ptprcap"       "Igj"           "Sh2d3c"        "Rdh12"        
[29] "2010001M09Rik" "Chi3l1"       
[1] ""
[1] ""
[1] "PC2"
 [1] "Plac8"         "Srgn"          "Elane"         "Tspan32"       "Ctsg"          "Bcat2"         "Cmtm7"        
 [8] "Prtn3"         "B4galnt1"      "Nkg7"          "Gstm1"         "Cdc20"         "Cst7"          "Clec12a"      
[15] "Ms4a3"         "Fcgr3"         "Ramp1"         "Cenpa"         "Prss57"        "Mcfd2"         "Calr"         
[22] "Etfb"          "Ptgr1"         "Pebp1"         "Idh1"          "1190002H23Rik" "Ccnb1"         "Vcam1"        
[29] "Ddx39"         "Farsa"        
[1] ""
 [1] "Hist1h4d"  "Hist1h1e"  "Hist1h2ab" "Rrm2"      "Ltf"       "Tyms"      "Smc6"      "Hist2h2ac" "Tcf19"     "Tubb5"    
[11] "Whsc1"     "Mthfd2"    "Nrm"       "Sod1"      "Atad2"     "Lig1"      "Syne2"     "Tipin"     "Pbk"       "Ccne1"    
[21] "Ncaph"     "Ccnf"      "Rbl1"      "Rfc2"      "Ccne2"     "Rpa1"      "Ncapd3"    "Trmt2b"    "Mapk13"    "Tubgcp2"  
[1] ""
[1] ""
[1] "PC3"
 [1] "Kpna2"   "Ccna2"   "Ube2c"   "Kif11"   "Tubb4b"  "Plk1"    "Tuba1c"  "Hiatl1"  "Aurka"   "Kif20a"  "Kif22"   "Ccnb1"  
[13] "Cenpf"   "Smtn"    "Bub1b"   "Plcg2"   "Csde1"   "Tpx2"    "Amfr"    "Atp2c1"  "Hn1"     "Ccnf"    "Ncaph"   "Tubb5"  
[25] "Cdc20"   "Cdca2"   "Slc40a1" "Slc28a2" "Slc2a3"  "Copg2"  
[1] ""
 [1] "Rpa2"          "Cdc6"          "Cdt1"          "Tcf19"         "Ftl1"          "Cdk4"          "Paics"        
 [8] "Gstm1"         "Hat1"          "C330006A16Rik" "Napsa"         "Chaf1b"        "Runx3"         "Akr1a1"       
[15] "Mcm3"          "Lig1"          "Rfc2"          "Dtl"           "Ctsh"          "Ube2g2"        "E2f1"         
[22] "5830415F09Rik" "Pold2"         "Ccne1"         "Slc25a5"       "Slc43a3"       "Npm1"          "Psmc3ip"      
[29] "Aldh9a1"       "Set"          
[1] ""
[1] ""
[1] "PC4"
 [1] "Srm"       "Nt5dc2"    "Hist2h2ac" "Ctsg"      "Hist1h1e"  "Hist1h2ab" "Hist1h4d"  "Galk1"     "Tmem97"    "Phgdh"    
[11] "Morf4l2"   "Calr"      "Ndc80"     "Ncl"       "C1qbp"     "Cst7"      "Pebp1"     "Set"       "Aurkb"     "Qdpr"     
[21] "Nrm"       "G6pc3"     "Fkbp4"     "Ppan"      "Idh2"      "Npm1"      "Mif"       "Pcgf5"     "Plbd1"     "Myb"      
[1] ""
 [1] "Gpx3"   "Ftl1"   "Rpsa"   "Rpl18a" "Cd177"  "Il1r2"  "Mcm2"   "Lrg1"   "Ccne2"  "Mcm6"   "Cxcr2"  "Dtl"    "Chi3l1" "Ikbke" 
[15] "Esyt1"  "Spp1"   "Mcm4"   "Scarb1" "Rplp0"  "Cdc6"   "Mpeg1"  "Ptgr1"  "Amfr"   "Csf2rb" "Man2b1" "Mcm3"   "Sirpa"  "Nt5c3" 
[29] "Eef1a1" "Slc2a3"
[1] ""
[1] ""
[1] "PC5"
 [1] "Ptprcap"       "Mif"           "Cd74"          "Hsp90b1"       "Ly6d"          "H2-Eb1"        "C1qbp"        
 [8] "Phgdh"         "Txndc5"        "Ly6e"          "Lgals3"        "Ifitm6"        "Plbd1"         "Ly6a"         
[15] "Herpud1"       "Sdf2l1"        "Gaa"           "Igj"           "Myb"           "2010001M09Rik" "Hspa5"        
[22] "Irf8"          "St3gal5"       "Myc"           "Creld2"        "Srm"           "Calr"          "Pdia4"        
[29] "Manf"          "Pdia6"        
[1] ""
 [1] "Nkg7"          "Elane"         "Prtn3"         "Rpl3"          "Hist1h4d"      "Fzr1"          "Hist1h1e"     
 [8] "Ms4a3"         "Rpl18a"        "Per3"          "Chit1"         "Hist2h2ac"     "Ldhb"          "Khk"          
[15] "Hmbs"          "Rpsa"          "Tubb5"         "Hist1h2ab"     "Ankle1"        "D030056L22Rik" "Nudt3"        
[22] "Ndc80"         "Mfsd10"        "Cdc20"         "Cenpa"         "Clec12a"       "Tmem127"       "Nt5c3"        
[29] "Tuba1c"        "Rnasel"       
[1] ""
[1] ""

subtiss7 <- ProjectPCA(object = subtiss7, do.print = FALSE)

Run Principal Component Analysis.

subtiss7 <- RunPCA(object = subtiss7, do.print = FALSE)
subtiss7 <- ProjectPCA(object = subtiss7, do.print = FALSE)

# If this fails for your subset, it may be that cells.use is more cells than you have left! Try reducing it.
PCHeatmap(object = subtiss7, pc.use = 1:3, cells.use = 250, do.balanced = TRUE, label.columns = FALSE, num.genes = 12)

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

PCElbowPlot(object = subtiss7)

Choose the number of principal components to use.

# Set number of principal components. 
sub7.n.pcs = 5

The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.

# Set resolution 
sub.res.used <- 1
subtiss7 <- FindClusters(object = subtiss7, reduction.type = "pca", dims.use = 1:sub7.n.pcs, 
    resolution = sub.res.used, print.output = 0, save.SNN = TRUE)

To visualize

# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
subtiss7 <- RunTSNE(object = subtiss7, dims.use = 1:sub7.n.pcs, seed.use = 10, perplexity=30)

# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = subtiss7, do.label = T)

subtiss7.markers <- FindAllMarkers(object = subtiss7, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

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subtiss7.markers %>% group_by(cluster) %>% top_n(6, avg_diff)

Check expression of genes of interset.

All cells have the same value of Klrb1a.All cells have the same value of Klrb1c.

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

How big are the clusters?

table(subtiss7@ident)

 0  1  2  3 
68 62 48 32 

Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.

TSNEPlot(object = subtiss7, do.return = TRUE, group.by = "plate.barcode")

Print a table showing the count of cells in each identity category from each plate.

table(as.character(subtiss7@ident), as.character(subtiss7@meta.data$plate.barcode))

   
    D041912 D042193 D045139 D045140 MAA000409 MAA000639 MAA000652 MAA000844 MAA001844 MAA001887
  0      14       1       8       6        19        18         0         1         1         0
  1      31       0       2       2         5        14         1         1         2         4
  2      23       0       5       1        10         9         0         0         0         0
  3       1       1       0       1        24         5         0         0         0         0

Assigning subannotations

For the subsets, we produce subannotations. These will be written back as metadata in the original object, so we can see all subannotations together.

If some of the clusters you find in the subset deserve additional annotation, you can add that right here. Use NA for clusters for which no subannotation is needed.

subcluster.ids <- c(0, 1, 2, 3, 4, 5)
subtiss7@meta.data[,'annotation'] <- plyr::mapvalues(x = subtiss7@ident, from = subcluster.ids, to = subannotation)

The following `from` values were not present in `x`: 4, 5

subtiss7@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = subtiss7@ident, from = subcluster.ids, to = cell_ontology_id)

The following `from` values were not present in `x`: 4, 5

tiss@meta.data[subtiss7@cell.names,'annotation'] <- as.character(subtiss7@meta.data$annotation)

invalid factor level, NA generated

tiss@meta.data[subtiss7@cell.names,'cell_ontology_id'] <- as.character(subtiss7@meta.data$cell_ontology_id)

invalid factor level, NA generated

subannotation <-
  c("T cell",
  "natural killer cell",
  "natural killer cell",
  "T cell",
  "T cell",
  "T cell")
  cell_ontology_id = c("CL:0000084",
  "CL:0000623",
  "CL:0000623",
  "CL:0000084",
  "CL:0000084",
  "CL:0000084")

When you save the subtissue, please give it a name.

Export the final metadata

So that Biohub can easily combine all your annotations, please export them as a simple csv.